single-cell-atac-seq-peak-calling-annotaion

single-cell-atac-seq-peak-calling-annotaion is a skill for Claude Code, Codex from PharMolix/OpenBioMed. It costs 0 tokens per session (3,167 once invoked), scanned A, original, MIT.

A workflow for studying open chromatin in ATAC-seq data, which shows where DNA is accessible for regulation. It finds accessible regions, links them to genes and genomic features, and compares them between experimental conditions.

In plain words
What is it for?
Finding peaks in BAM files, building a consensus peak set, counting reads, annotating promoters and other regions, and identifying regions with different accessibility.
Why use it?
It applies ATAC-seq-specific processing, removes known problematic genome regions, creates a shared set of regions across samples, and corrects for multiple statistical tests.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Finding peaks in BAM files, building a consensus peak set, counting reads, annotating promoters and other regions, and identifying regions with different accessibility.

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Install with agentmods
npx agentmods add skills/pharmolix/openbiomed/single-cell-atac-seq-peak-calling-annotaion
About the project

OpenBioMed is an agent platform and toolkit collection for biomedical research and drug discovery, covering areas such as molecular design, protein analysis, and single-cell data analysis. It is intended for researchers and provides the biomedical skills listed in the catalogue as workflows for Claude Code.

PharMolix/OpenBioMed · 1,105 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add PharMolix/OpenBioMed --skill single-cell-atac-seq-peak-calling-annotaion
Clone the repo
git clone --depth 1 https://github.com/PharMolix/OpenBioMed

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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Your own site
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Your own site · 80×15
<a href="https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-atac-seq-peak-calling-annotaion"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-atac-seq-peak-calling-annotaion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,167 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.03167
Opus 5 $0.00000 $0.01584
Sonnet 5 $0.00000 $0.00633
Haiku 4.5 $0.00000 $0.00317

Measured 13d ago against content hash 5cf62fc2edce, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

single-cell-atac-seq-peak-calling-annotaion scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/single-cell-atac-seq-peak-calling-annotaion/SKILL.md · 294 lines

How it starts

The opening of the file, as written. The whole thing — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ATAC-seq Peak Calling and Differential Accessibility

Call accessible chromatin peaks from ATAC-seq BAM files, annotate peaks to genomic features and genes, and identify differentially accessible regions between experimental conditions.

This is Step 2 of the bulk ATAC-seq pipeline.


What it does

  1. Calls peaks with MACS2 (--nomodel --shift -100 --extsize 200) optimized for ATAC-seq
  2. Filters peaks overlapping ENCODE blacklist regions
  3. Builds a consensus peak set across all samples using bedtools merge
  4. Counts reads per sample in consensus peaks (featureCounts or bedtools coverage)
  5. Annotates peaks with genomic features (promoter, UTR, exon, intron, intergenic) and nearest gene
  6. Runs differential accessibility analysis with DESeq2 on pseudobulk counts (recommended) or DiffBind
  7. Applies FDR correction and generates ranked DAR table and volcano plot

Why this exists

If you ask a general AI to "call peaks from ATAC-seq data," it will:

  • Use default MACS2 parameters (designed for ChIP-seq), not ATAC-seq-specific parameters
  • Not build a consensus peak set across samples, making cross-sample comparison impossible
  • Not remove ENCODE blacklist regions, leaving artifactual peaks from repetitive elements
  • Use --format BAM instead of --format BAMPE (ATAC-seq is paired-end)
  • Apply DESeq2 directly to individual reads per cell instead of pseudobulk aggregation for multi-sample data

This skill encodes the correct methodological decisions:

  • Uses ATAC-seq-specific MACS2 flags: --nomodel --shift -100 --extsize 200 --format BAMPE
  • Filters ENCODE blacklist regions (hg38 or mm10) that produce artifactual signal
  • Builds a reproducible consensus peak set using IDR or bedtools merge across replicates
  • Applies pseudobulk DESeq2 for multi-sample differential analysis (controls type I error)

Reference Methods

MACS2 ATAC-seq parameters:

  • --nomodel: Skip the ChIP enrichment model building (ATAC-seq does not have a broad enrichment model)
  • --shift -100 --extsize 200: Centers signal on Tn5 cut site by shifting reads 100 bp upstream and extending 200 bp
  • --format BAMPE: Reads paired-end fragment coordinates directly from BAM
  • --nolambda: Disables local lambda background estimation (optional; use with high-coverage data)

Read the full file on GitHub · 294 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 13d ago First seen · 294 lines · 0 tokens per session scan A 5cf62fc2edce

Subscribe to this mod's changes

single-cell-atac-seq-peak-calling-annotaion is a skill published in the GitHub repository PharMolix/OpenBioMed (1,105 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,167 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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